Qualcomm Incorporated

Snap Inc.

Daqri, LLC

Facebook Technologies, LLC

Meta Platforms Technologies, LLC

Daniel Wagner

Matthias Kalkgruber

Alessandro Mulloni

Kai Zhou

Qi Pan

Dominik Schnitzer

Erick Mendez Mendez

Clemens Birklbauer

Gerhard Reitmayr

Jeroen Diederik Hol

Georg Halmetschlager-Funek

Bernhard Jung

Sagi Katz

Dieter Schmalstieg

Daniel Wolf

Georgios Evangelidis

Patrick Timothy Mcsweeney Simons

Branislav Micusik

Alex Feinman

Weston Welge

Zsolt Szalavari

Youngmin Park

Heinrich Fink

Igor Komir

Ilteris Kaan Canberk

Gerald Nilles

Jason Heger

Mahesh Ramachandran

Dmitry Ryuma

Michael Gervautz

Amit Singh

Jakob Zillner

Lucas Kazansky

Kiyoung Kim

Olha Borys

Tobias Langlotz

Daniel Knoblauch

Jeffrey Michael DeWall

Erick Mendez

Luca Ricci

Brian Mullins

Murali Chari

Serafin Diaz Spindola

Daniel Colascione

Roy Lawrence Ashok Inigo

Frank Chester Irving, Jr

Prince Gupta

Slawomir K Grzechnik

Christopher Michaels Garcia

Jonathan Trevor Freeman

Michael Kozlowski

Jan Bajana

Xuan Wang

Eyewear devices that include two SoCs that share processing workload. Instead of using a single SoC located either on the left or right side of the eyewear devices, the two SoCs have different assigned responsibilities to operate different devices and perform different processes to balance workload. In one example, the eyewear device utilizes a first SoC to operate the OS, a first color camera, a second color camera, a first display, and a second display. A second SoC is configured to run computer vision (CV) algorithms, visual odometry (VIO), tracking hand gestures of the user, and providing depth from stereo. This configuration provides organized logistics to efficiently operate various features, and balanced power consumption.

Dual system on a chip eyewear

Disclosed are systems, methods, and non-transitory computer-readable media for continuous surface and depth estimation. A continuous surface and depth estimation system determines the depth and surface normal of physical objects by using stereo vision limited within a predetermined window.

Continuous surface and depth estimation

Visual-inertial tracking of an eyewear device using a rolling shutter camera(s). The device includes a position determining system. Visual-inertial tracking is implemented by sensing motion of the device. An initial pose is obtained for a rolling shutter camera and an image of an environment is captured. The image includes feature points captured at a particular capture time. A number of poses for the rolling shutter camera is computed based on the initial pose and sensed movement of the device. The number of computed poses is responsive to the sensed movement of the mobile device. A computed pose is selected for each feature point in the image by matching the particular capture time for the feature point to the particular computed time for the computed pose. The position of the mobile device is determined within the environment using the feature points and the selected computed poses for the feature points.

Visual-inertial tracking using rolling shutter cameras

An eyewear device with flexible frame for Augmented Reality (AR) is disclosed. At least two sensors and a display are mounted on the flexible frame. When in use, the real time geometry of the eyewear device may change from factory calibrated geometry, resulting in low quality AR rendering. A modeling module is provided to model the real time geometry of the eyewear device on the fly using sensor information of the at least two sensors. The modeled real time geometry is then provided to a rendering module to accurately display the AR to the user.

Geometry modeling of eyewear devices with flexible frames

Eyewear providing an interactive augmented reality experience between two eyewear devices by using alignment between respective 6DOF trajectories, also referred to herein as ego motion alignment. An eyewear device of user A and an eyewear device of user B track the eyewear device of the other user, or an object of the other user, such as on the user's face, to provide the collaborative AR experience. This enables sharing common three-dimensional content between multiple eyewear users without using or aligning the eyewear devices to common image content such as a marker, which is a more lightweight solution with reduced computational burden on a processor. An inertial measurement unit may also be used to align the eyewear devices.

Collaborative augmented reality eyewear with ego motion alignment

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

Varied depth determination using stereo vision and phase detection auto focus (PDAF)

A method for configuring a digital light projector (DLP) of an augmented reality (AR) display device is described. A light source component of the DLP projector is configured to generate a single red-green-blue color sequence repetition per image frame. The AR display device identifies a color sequence of the light source component of the DLP projector and tracks a motion of the AR display device. The AR display device adjusts an operation of the DLP projector based on the single red-green-blue color sequence repetition, the color sequence of the light source component of the DLP projector, and the motion of the AR display device.

System for using digital light projectors for augmented reality

A method for minimizing latency of moving objects in an augmented reality (AR) display device is described. In one aspect, the method includes determining an initial pose of a visual tracking device, identifying an initial location of an object in an image that is generated by an optical sensor of the visual tracking device, the image corresponding to the initial pose of the visual tracking device, rendering virtual content based on the initial pose and the initial location of the object, retrieving an updated pose of the visual tracking device, tracking an updated location of the object in an updated image that corresponds to the updated pose, and applying a time warp transformation to the rendered virtual content based on the updated pose and the updated location of the object to generate transformed virtual content.

Late warping to minimize latency of moving objects

Visual-inertial tracking of an eyewear device using a rolling shutter camera(s). The eyewear device includes a position determining system. Visual-inertial tracking is implemented by sensing motion of the eyewear device. An initial pose is obtained for a rolling shutter camera and an image of an environment is captured. The image includes feature points captured at a particular capture time. A number of poses for the rolling shutter camera is computed based on the initial pose and sensed movement of the device. The number of computed poses is responsive to the sensed movement of the mobile device. A computed pose is selected for each feature point in the image by matching the particular capture time for the feature point to the particular computed time for the computed pose. The position of the mobile device is determined within the environment using the feature points and the selected computed poses for the feature points.

A method for reducing motion-to-photon latency for hand tracking is described. In one aspect, a method includes accessing a first frame from a camera of an Augmented Reality (AR) device, tracking a first image of a hand in the first frame, rendering virtual content based on the tracking of the first image of the hand in the first frame, accessing a second frame from the camera before the rendering of the virtual content is completed, the second frame immediately following the first frame, tracking, using the computer vision engine of the AR device, a second image of the hand in the second frame, generating an annotation based on tracking the second image of the hand in the second frame, forming an annotated virtual content based on the annotation and the virtual content, and displaying the annotated virtual content in a display of the AR device.

Low latency hand-tracking in augmented reality systems

Eyewear including a frame having a first side and a second side, a first temple extending from the first side of the frame, a second temple extending from the second side of the frame, electronic components, a first system on a chip (SoC) adjacent the first side of the frame coupled to a first set of the electronic components, and a second system on a chip adjacent the second side, the second SoC coupled to the first SoC and to a second set of the plurality of electronic components. Processing workloads are balanced between the first SoC and the second SoC by performing a first set of operations with the first SoC and performing a second set of operations with the second SoC.

Eyewear devices that include two SoCs that share processing workload. Instead of using a single SoC located either on the left or right side of the eyewear devices, the two SoCs have different assigned responsibilities to operate different devices and perform different processes to balance workload. In one example, the eyewear device utilizes a first SoC to operate a first color camera, a second color camera, a first display, and a second display. The first SoC and a second SoC are configured to selectively operate a first and second computer vision (CV) camera algorithms. The first SoC is configured to perform visual odometry (VIO), track hand gestures of the user, and provide depth from stereo images. This configuration provides organized logistics to efficiently operate various features, and balanced power consumption.

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using, stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

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Vienna, Austria

Daniel Wagner